U.S. patent number 8,615,458 [Application Number 13/776,178] was granted by the patent office on 2013-12-24 for industry size of wallet.
This patent grant is currently assigned to American Express Travel Related Services Company, Inc.. The grantee listed for this patent is American Express Travel Related Services Company, Inc.. Invention is credited to Karlyn Heiner Crotty, Iwao Fusillo, Prashant Kalia.
United States Patent |
8,615,458 |
Kalia , et al. |
December 24, 2013 |
Industry size of wallet
Abstract
Consumer spend by industry is modeled based on the industry
sizes of wallet of consumers having a high share of wallet with a
financial institution. A size of wallet is calculated for each
consumer in a plurality of consumers. A share of wallet for each
consumer is also calculated. A subset of the plurality of consumers
whose share of wallet is above a given percentage of their size of
wallet is then determined. For each consumer in the subset, an
industry size of wallet is determined. A correlation between the
industry size of wallet of a given consumer and one or more
characteristics of the given consumer is then derived using the
industry size of wallet for the consumers in the subset.
Inventors: |
Kalia; Prashant (Fair Lawn,
NJ), Crotty; Karlyn Heiner (Pennington, NJ), Fusillo;
Iwao (Merrick, NY) |
Applicant: |
Name |
City |
State |
Country |
Type |
American Express Travel Related Services Company, Inc. |
New York |
NY |
US |
|
|
Assignee: |
American Express Travel Related
Services Company, Inc. (New York, NY)
|
Family
ID: |
39476956 |
Appl.
No.: |
13/776,178 |
Filed: |
February 25, 2013 |
Prior Publication Data
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Document
Identifier |
Publication Date |
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US 20130191262 A1 |
Jul 25, 2013 |
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Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
Issue Date |
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13538936 |
Jun 29, 2012 |
8401947 |
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11608179 |
Aug 7, 2012 |
8239250 |
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60868229 |
Dec 1, 2006 |
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Current U.S.
Class: |
705/35; 705/37;
705/36R |
Current CPC
Class: |
G06Q
30/00 (20130101); G06Q 30/0201 (20130101); G06Q
40/00 (20130101); G06Q 30/0205 (20130101); G06Q
30/0202 (20130101); G06Q 10/06375 (20130101) |
Current International
Class: |
G06Q
40/00 (20120101) |
Field of
Search: |
;705/35,36R,37 |
References Cited
[Referenced By]
U.S. Patent Documents
Foreign Patent Documents
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2001282957 |
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Oct 2001 |
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JP |
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2002163449 |
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Jun 2002 |
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JP |
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2003316950 |
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Nov 2003 |
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JP |
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0116896 |
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Mar 2001 |
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WO |
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0139090 |
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May 2001 |
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WO |
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0180053 |
|
Oct 2001 |
|
WO |
|
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Primary Examiner: Nguyen; Nga B.
Attorney, Agent or Firm: Snell & Wilmer L.L.P.
Parent Case Text
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation of U.S. patent application Ser.
No. 13/538,936, filed Jun. 29, 2012 and entitled "INDUSTRY SIZE OF
WALLET" which is incorporated by reference herein in its entirety.
The '936 application is a continuation of U.S. patent application
Ser. No. 11/608,179, now U.S. Pat. No. 8,239,250, filed Dec. 7,
2006 and entitled "INDUSTRY SIZE OF WALLET" which is incorporated
by reference herein in its entirety. The '179 Application claims
the benefit of U.S. Provisional Patent Application No. 60/868,229,
filed Dec. 1, 2006 and entitled "INDUSTRY SIZE OF WALLET" which is
incorporated by reference herein in its entirety.
Claims
What is claimed is:
1. A method comprising: calculating, by a computer-based system for
modeling consumer spend comprising a processor and a tangible,
non-transitory memory, a size of wallet for each consumer in a
plurality of consumers; and determining, by the computer-based
system, an industry size of wallet for each consumer using a fixed
weighting factor and a graded weighting factor in conjunction with
lifestyle variables comprising at least one of a location rank, a
length of each consumer's tenure with a credit bureau, each
consumer's gender, and each consumer's household size, wherein the
graded weighting factor varies in accordance with the value of at
least one of the lifestyle variables.
2. The method of claim 1, wherein the calculating the size of
wallet for each consumer in the plurality of consumers comprises
modeling, by the computer-based system, spending patterns using
internal customer data and consumer panel data.
3. The method of claim 1, wherein the calculating the size of
wallet for each consumer in the plurality of consumers comprises
estimating, by the computer-based system, credit-related
information of each consumer based on tradeline data of each
consumer, previous balance transfers of each consumer, and a model
of consumer spending patterns to arrive at estimated credit-related
information.
4. The method of claim 3, wherein the calculating the size of
wallet for each consumer in the plurality of consumers further
comprises offsetting, by the computer-based system, the previous
balance transfers from the estimated credit-related
information.
5. The method of claim 1, further comprising: calculating the share
of wallet comprises calculating a share of wallet associated with a
given financial institution; and determining a subset of the
plurality of consumers comprises identifying consumers whose share
of wallet associated with the financial institution is greater than
approximately 90%.
6. The method of claim 1, wherein the determining an industry size
of wallet comprises: determining the amount of spend within the
industry using one or more accounts associated with a financial
institution; and equating the amount of spend within the industry
with the industry size of wallet.
7. The method of claim 1, further comprising: determining, by the
computer-based system, a subset of the plurality of consumers whose
share of wallet is above a given percentage of their size of
wallet; deriving, by the computer-based system, a correlation
between an industry size of wallet for a given consumer and one or
more characteristics of the given consumer using the industry size
of wallet for the consumers in the subset, wherein the
characteristics of the consumer include at least one of: total size
of wallet of the consumer; residence location; credit bureau
tenure; age; gender; household size; and number of active
transaction cards in a household of the consumer.
8. The method of claim 7, wherein the deriving a correlation
comprises: identifying consumers having substantially similar
industry sizes of wallet; and examining spend habits of the
identified consumers to ascertain common characteristics that
influence spend in the industry.
9. The method of claim 1, wherein the industry is at least one of a
travel industry, a restaurant industry, or an everyday spend
industry.
10. The method of claim 1, wherein the industry is at least one of
an airline industry, a lodging industry, or a vehicle rental
industry.
11. The method of claim 7, further comprising developing a model
based on correlations between the industry size of wallet and the
characteristics of the consumer.
12. A method comprising: calculating, by a computer-based system
for targeting consumers comprising a processor and a tangible,
non-transitory memory, a total share of wallet associated with a
financial institution for one or more consumers; estimating, by the
computer-based system, an industry size of wallet of each consumer;
and calculating, by the computer-based system, an external size of
the industry size of wallet of each consumer using a fixed
weighting factor and a graded weighting factor in conjunction with
lifestyle variables comprising at least one of a location rank, a
length of each consumer's tenure with a credit bureau, each
consumer's gender, and each consumer's household size, wherein the
graded weighting factor varies in accordance with the value of at
least one of the lifestyle variables.
13. The method of claim 12, wherein the industry size of wallet of
each consumer is calculated by modeling, by the computer-based
system, spending patterns using internal customer data, and
consumer panel data.
14. The method of claim 12, wherein the industry size of wallet of
each consumer is calculated by: estimating, by the computer-based
system, credit-related information of each consumer based on
tradeline data of each consumer, previous balance transfers of each
consumer, and a model of consumer spending patterns to arrive at
estimated credit-related information; and offsetting, by the
computer-based system, the previous balance transfers from the
estimated credit-related information.
15. The method of claim 12, further comprising targeting, by the
computer-based system, one or more consumers having a relatively
high external size of the industry size of wallet and a given
minimal total share of wallet with offers to increase an industry
share of wallet associated with the financial institution.
16. The method of claim 12, wherein the calculating an external
size of the industry size of wallet of the consumer comprises
subtracting industry spend of the consumer associated with the
financial institution from the industry size of wallet of the
consumer.
17. The method of claim 12, wherein the calculating an external
size of the industry size of wallet of the consumer further
comprises determining the amount of industry spend of the consumer
associated with the financial institution from internal records of
the financial institution.
18. The method of claim 15, wherein the targeting comprises
targeting the consumer with an offer for a new product.
19. The method of claim 15, wherein the targeting one or more
consumers comprises targeting the consumer with an incentive to
increase spending on an existing product associated with the
financial institution.
20. A computer readable storage medium bearing instructions, the
instructions, when executed by a processor for modeling consumer
spend by industry, cause said processor to perform operations
comprising: calculating, by the processor, a size of wallet for
each consumer in a plurality of consumers; and determining, by the
processor, an industry size of wallet for each consumer using a
fixed weighting factor and a graded weighting factor in conjunction
with lifestyle variables comprising at least one of a location
rank, a length of each consumer's tenure with a credit bureau, each
consumer's gender, and each consumer's household size, wherein the
graded weighting factor varies in accordance with the value of at
least one of the lifestyle variables.
Description
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present application relates to financial data processing, in
particular customer modeling and behavioral analysis.
2. Background Art
It is axiomatic that consumers will tend to spend more when they
have greater purchasing power. The capability to accurately
estimate a consumer's spend capacity could therefore allow a
financial institution (such as a credit company, lender or any
consumer services company) to better target potential prospects and
identify any opportunities to increase consumer transaction
volumes, without an undue increase in the risk of defaults.
Consumers will be most attracted to products that are customized
specifically for their individual interests and spending patterns.
Attracting additional consumer spending in this manner, in turn,
would increase such financial institution's revenues, primarily in
the form of an increase in transaction fees and interest payments
received. Consequently, a consumer model that can accurately
estimate purchasing power and identify industries in which the
consumer is most interested in spending is of paramount interest to
many financial institutions and other consumer services
companies.
A limited ability to estimate consumer spend behavior from
point-in-time credit data has previously been available. A
financial institution can, for example, simply monitor the balances
of its own customers' accounts. When a credit balance is lowered,
the financial institution could then assume that the corresponding
consumer now has greater purchasing power. Sikh an assumption has
its flaws, however. For example, it is oftentimes difficult to
confirm whether the lowered balance is the result of a balance
transfer to another account. Such balance transfers represent no
increase in the consumer's capacity to spend, and so this simple
model of consumer behavior has its flaws.
In order to achieve a complete picture of any consumer's purchasing
ability and interests, one must examine in detail the full range of
a consumer's financial accounts, including credit accounts,
checking and savings accounts, investment portfolios, and the like.
However, the vast majority of consumers do not maintain all such
accounts with the same financial institution and the access to
detailed financial information from other financial institutions is
restricted by consumer privacy laws, disclosure policies and
security concerns.
There is limited and incomplete consumer information from credit
bureaus and the like at the aggregate and individual consumer
levels. Since balance transfers are nearly impossible to
consistently identify from the ace of such records, this
information has not previously been enough to obtain accurate
estimates of a consumer's actual spending ability.
Accordingly, there is a need for a method and apparatus for
determining a customer's size of wallet along with specific
industries in which the customer is most likely to spend which
addresses certain problems of existing technologies.
SUMMARY OF THE INVENTION
In one embodiment of the present invention, consumer spend by
industry is modeled based on the industry sizes of wallet of
consumers having a high share of wallet with a financial
institution. A size of wallet is calculated for each consumer in a
plurality of consumers. A share of wallet for each consumer is also
calculated. A subset of the plurality of consumers whose share of
wallet is above a given percentage of their size of wallet is then
determined. For each consumer in the subset, an industry size of
wallet is determined. A correlation between the industry size of
wallet of a given consumer and one or more characteristics of the
given consumer is then derived using the industry size of wallet
for the consumers in the subset.
In another embodiment of the present invention, a customer can be
targeted with an offer to increase the customer's industry share of
wallet associated with a given financial institution. To do this,
an industry size of wallet is estimated for one or more consumers.
The external size of the industry size of wallet of each consumer
is calculated, and one or more consumers having a relatively high
external size of the industry wallet (that is, potential) and a
reasonably high total share of wallet with the financial
institution (that is, engagement with the financial institution)
are targeted with offers to increase their industry share of wallet
associated with the financial institution.
BRIEF DESCRIPTION OF THE DRAWINGS/FIGURES
The accompanying drawings, which are incorporated herein and form a
part of the specification, illustrate the present invention and,
together with the description, further serve to explain the
principles of the invention and to enable a person skilled in the
pertinent art to make and use the invention.
FIG. 1 is a flowchart of an exemplary process for creating an
industry size of wallet model.
FIG. 2 is a flowchart of an exemplary process for targeting a
consumer with an offer to increase spending.
FIG. 3 is a graph of average travel size of wallet by residence
location.
FIG. 4 is a graph of average restaurant size of wallet by residence
location.
FIG. 5 is graph of average industry size of wallet relative to a
consumer's total size of wallet.
FIG. 6 is a graph of average industry size of wallet relative to a
consumer's credit bureau tenure.
FIG. 7 is a graph of average industry size of wallet relative to a
consumer's gender.
FIG. 8 is a graph of average everyday spend size of wallet by
number of individuals in a household.
FIG. 9 is a graph of average everyday spend size of wallet by
number of active transaction cards in a household.
FIG. 10 is a graph illustrating the predictions of an exemplary
size of travel wallet model against actual travel spend.
FIG. 11 is a graph illustrating the predictions of an exemplary
size of restaurant wallet model against actual restaurant
spend.
FIG. 12 is a graph illustrating the predictions of an exemplary
size of everyday spend wallet model against actual everyday
spend.
FIG. 13 is a block diagram of an exemplary computer system useful
for implementing the present invention.
The present invention will be described with reference to the
accompanying drawings. The drawing in which an element first
appears is typically indicated by the leftmost digit(s) in the
corresponding reference number.
DETAILED DESCRIPTION OF THE INVENTION
I. Overview
While specific configurations and arrangements are discussed, it
should be understood that this is done for illustrative purposes
only. A person skilled in the pertinent art will recognize that
other configurations and arrangements can be used without departing
from the spirit and scope of the present invention. It will be
apparent to a person skilled in the pertinent art that this
invention can also be employed in a variety of other
applications.
The terms "user," "end user," "consumer," "customer,"
"participant," and/or the plural form of these terms are used
interchangeably throughout herein to refer to those persons or
entities capable of accessing, using, being affected by and/or
benefiting from the tool that the present invention provides for
determining a household size of wallet.
Furthermore, the terms "business" or "merchant" may be used
interchangeably with each other and shall mean any person, entity,
distributor system, software and/or hardware that is a provider,
broker and/or any other entity in the distribution chain of goods
or services. For example, a merchant may be a grocery store, a
retail store, a travel agency, a service provider, an on-line
merchant or the like.
1. Transaction Accounts and Instrument
A "transaction account" as used herein refers to an account
associated with an open account or a closed account system (as
described below). The transaction account may exist in a physical
or non-physical embodiment. For example, a transaction account may
be distributed in non-physical embodiments such as an account
number, frequent-flyer account, telephone calling account or the
like. Furthermore, a physical embodiment of a transaction account
may be distributed as a financial instrument.
A financial transaction instrument may be traditional plastic
transaction cards, titanium-containing, or other metal-containing,
transaction cards, clear and/or translucent transaction cards,
foldable or otherwise unconventionally-sized transaction cards,
radio-frequency enabled transaction cards, or other types of
transaction cards, such as credit, charge, debit, pre-paid or
stored-value cards, or any other like financial transaction
instrument. A financial transaction instrument may also have
electronic functionality provided by a network of electronic
circuitry that is printed or otherwise incorporated onto or within
the transaction instrument (and typically referred to as a "smart
card"), or be a fob having a transponder and an RFID reader.
2. Use of Transaction Accounts
With regard to use of a transaction account, users may communicate
with merchants in person (e.g., at the box office), telephonically,
or electronically (e.g., from a user computer via the Internet).
During the interaction, the merchant may offer goods and/or
services to the user. The merchant may also offer the user the
option of paying for the goods and/or services using any number of
available transaction accounts. Furthermore, the transaction
accounts may be used by the merchant as a form of identification of
the user. The merchant may have a computing unit implemented in the
form of a computer-server, although other implementations are
possible.
In general, transaction accounts may be used for transactions
between the user and merchant through any suitable communication
means, such as, for example, a telephone network, intranet, the
global, public Internet, a point of interaction device (e.g., a
point of sale (POS) device, personal digital assistant (PDA),
mobile telephone, kiosk, etc.), online communications, off-line
communications, wireless communications, and/or the like.
A transaction account has a basic user, who is the primary user
associated with the account. A transaction account may also have a
supplemental user who is given access to the account by the basic
user. The supplemental user may possess a duplicate of the
transaction instrument associated with the account.
3. Account and Merchant Numbers
An "account," "account number" or "account code", as used herein,
may include any device, code, number, letter, symbol, digital
certificate, smart chip, digital signal, analog signal, biometric
or other identifier/indicia suitably configured to allow a consumer
to access, interact with or communicate with a financial
transaction system. The account number may optionally be located on
or associated with any financial transaction instrument (e.g.,
rewards, charge, credit, debit, prepaid, telephone, embossed,
smart, magnetic stripe, bar code, transponder or radio frequency
card).
Persons skilled in the relevant arts will understand the breadth of
the terms used herein and that the exemplary descriptions provided
are not intended to be limiting of the generally understood
meanings attributed to the foregoing terms.
It is noted that references in the specification to "one
embodiment", "an embodiment", "an example embodiment", etc.,
indicate that the embodiment described may include a particular
feature, structure, or characteristic, but every embodiment may not
necessarily include the particular feature, structure, or
characteristic. Moreover, such phrases are not necessarily
referring to the same embodiment. Further, when a particular
feature, structure, or characteristic is described in connection
with an embodiment, it would be within the knowledge of one skilled
in the art to effect such feature, structure, or characteristic in
connection with other embodiments whether or not explicitly
described.
While specific configurations and arrangements are discussed, it
should be understood that this is done for illustrative purposes
only. A person skilled in the pertinent art will recognize that
other configurations and arrangements can be used without departing
from the spirit and scope of the present invention. It will be
apparent to a person skilled in the pertinent art that this
invention can also be employed in a variety of other
applications.
As used herein, the following terms shall have the following
meanings. A trade or tradeline refers to a credit or charge vehicle
issued to an individual customer by a credit grantor. Types of
tradelines include, for example and without limitation, bank loans,
credit card accounts, retail cards, personal lines of credit and
car loans/leases. For purposes here, use of the term credit card
shall be construed to include charge cards except as specifically
noted. Tradeline data describes the customer's account status and
activity, including, for example, names of companies where the
customer has accounts, dates such accounts were opened, credit
limits, types of accounts, balances over a period of time and
summary payment histories. Tradeline data is generally available
for the vast majority of actual consumers. Tradeline data, however,
does not include individual transaction data, which is largely
unavailable because of consumer privacy protections. Tradeline data
may be used to determine both individual and aggregated consumer
spending patterns, as described herein.
Consumer panel data measures consumer spending patterns from
information that is provided by, typically, millions of
participating consumer panelists. Such consumer panel data is
available through various consumer research companies, such as
comScore Networks, Inc. of Reston, Va. Consumer panel data may
typically include individual consumer information such as credit
risk scores, credit card application data, credit card purchase
transaction data, credit card statement views, tradeline types,
balances, credit limits, purchases, balance transfers, cash
advances, payments made, finance charges, annual percentage rates
and fees charged. Such individual information from consumer panel
data, however, is limited to those consumers who have participated
in the consumer panel, and so such detailed data may not be
available for all consumers.
Although the present invention is described as relating to
individual consumers, one of skill in the pertinent art(s) will
recognize that it can also apply to small businesses and
organizations without departing from the spirit and scope of the
present invention.
II. Industry Size of Wallet
Consumers tend to spend more when they have greater purchasing
power. It is thus advantageous for a financial institution (such as
a credit company, lender or any consumer services company) to
target existing customers and potential customers with
opportunities to increase their transaction volumes. The capability
to accurately estimate a consumer's spend capacity allows the
financial institution to target potential prospects and identify
any opportunities to increase consumer transaction volumes, without
the financial institution experiencing an undue increase in the
risk of defaults.
Additionally, consumers are most attracted to products that are
customized specifically for their individual interests and spending
patterns. Attracting additional consumer spending in this manner,
in turn, increases the financial institution's revenues, primarily
in the form of an increase in transaction fees and interest
payments received.
A model may be developed that correlates spending patterns of
consumers based on lifestyle characteristics of those consumers.
Lifestyle characteristics may include, for example and without
limitation, credit bureau tenure, age, gender, disposable income,
geographic location, household size, number of transaction cards in
a household, size of total spending wallet, and other third party
data, as will be discussed in further detail below. Once lifestyle
characteristics are identified as indicators of certain spending
patterns, consumers can be categorized based on their lifestyle
characteristics and the correlated spending patterns.
A. Model Development
A model for determining consumer spending patterns using various
lifestyle characteristics may be developed based on detailed
analysis of a number of consumers. Such a detailed analysis may
include determining the total size of wallet of the customer, as
well as ascertaining one or more lifestyle characteristics of the
customer, FIG. 1 is an illustration of an exemplary method 100 for
modeling consumer spending patterns using various lifestyle
characteristics.
In step 102, the total size of wallet is determined for a plurality
of consumers. The total size of wallet is the entire amount of
spend by a particular consumer from tradeline data sources over a
given period of time. The total size of wallet of a consumer may be
calculated based on, for example and without limitation, internal
customer tradeline data and/or external tradeline data available
from, for example, a credit bureau. An exemplary method of
calculating the size of wallet of an individual is described in
U.S. patent application Ser. No. 11/169,588, filed Jun. 30, 2005,
entitled Method and Apparatus for Consumer Interaction Based on
Spend Capacity, incorporated by reference herein in its
entirety.
Once the size of wallet has been calculated for a plurality of
consumers, method 100 proceeds to step 104. In step 104, a subset
of consumers having a high share of wallet with a particular
financial institution is identified. The share of wallet is the
portion of the spending wallet that is captured by the particular
financial institution. Consumers having a high share of wallet with
the particular financial institution may be those consumers whose
spend on accounts associated with the financial institution is more
than, for example, 90% of their total spend. This subset of
consumers is used by the financial institution in modeling consumer
behavior, because the financial institution typically has access to
most of the individual records of charge of the consumers and can
determine industry-related spending habits of the consumers.
Consumers having an extremely high share of wallet with the
financial institution (e.g., the top 1% of high-share consumers)
may be excluded from the modeling process, to eliminate
consideration of small business spending in the modeling
process.
After determining the high-share subset of consumers, method 100
proceeds to step 106. In step 106, an industry size of wallet is
calculated for each consumer. Information about the consumer's
spending in various industries can be obtained in a variety of
ways. As mentioned previously, since most of the spending of
high-share consumers is done with the financial institution, the
financial institution typically has a record of the consumer's
spend by industry. If such a record does not already exist, the
financial institution can, for example, analyze the records of
charge of each consumer in the subset of consumers to determine the
industry-related spending habits of each consumer. An industry is
the type of good or service purchased by the consumer. Types of
industries may include industries at a macro level, for example and
without limitation, the travel industry, the restaurant industry,
and the entertainment industry. Types of industries may also
include industries at a micro level, for example and without
limitation, the airline industry, the lodging industry, and the car
rental industry, each of which is a subset of a macro industry,
such as the travel industry. The industry related spending habits
of a consumer include, for example and without limitation, the
amount of spend in a given industry and the rate of spend in the
given industry. Although the present invention will mostly be
described with respect to spend in the travel industry, one of
skill in the relevant art(s) will recognize that the methods and
systems disclosed herein may involve spend in any other industry
without departing from the spirit and scope of the present
invention.
Because the subset of consumers has a high share of wallet with the
financial institution, it is reasonable to assume that the spending
habits identified for each consumer using the records of the
financial institution are reflective of the consumer's spending
habits across his or her entire spending wallet. For example, if a
person has a high share of wallet with the financial institution,
that person's travel spending on accounts associated with the
financial institution is likely approximately equal to his or her
total travel spending. The amount of industry spend by each
consumer in the high-share subset of consumers is deemed to be that
consumer's industry size of wallet.
Once the industry size of wallet of each consumer in the subset of
consumers has been determined, method 100 proceeds to step 108. In
step 108, relationships between the characteristics and an industry
size of wallet are identified. To identify these relationships, the
spend habits of multiple consumers are examined to ascertain
characteristics of the consumers that influence or are indicative
of spend in a given industry. These characteristics include, for
example and without limitation, financial and demographic
characteristics, and are referred to herein as lifestyle
characteristics. For example, if the financial institution wants to
determine what factors influence travel spending, profiles of
consumers who spend a high percentage of their wallet on travel can
be compared to identify common lifestyle characteristics. In
another example, profiles of consumers who spend a high percentage
of their wallet on travel can be compared to profiles of consumers
who spend a low percentage of their wallet on travel to identify
differentiating lifestyle characteristics.
Some lifestyle characteristics may have a given weight (e.g., the
magnitude of their effect on industry-related spend) regardless of
the actual value of the characteristic. Other lifestyle
characteristics may have a graded aspect to them, such that the
weight of the variable is dependent on the value of the variable.
An example lifestyle characteristic whose weight on airline spend
varies based on the value of the characteristic is the geographic
location of the consumer's residence. FIG. 3 is chart of
residential zip codes versus average travel-related spend by
residents of those zip codes. FIG. 3 takes into consideration the
high-share subset of consumers, and computes, for example, an
average airline spend value for each available zip code. As
illustrated in FIG. 3, residents of zip codes closer to airports
have more travel-related spend than residents of zip codes farther
away from airports. A correlation thus exists between specific zip
codes and the airline industry size of wallet, and the zip codes
can be ranked based on their average airline spend. In this manner,
the ranking becomes a variable indicative of airline spending.
The geographic location of the consumer's residence can also
influence restaurant spend, as illustrated in FIG. 4. FIG. 4 is a
chart of residential zip codes versus average restaurant-related
spend by residents of those zip codes. Correlations between
specific zip codes and restaurant spending can thus be
identified.
Other lifestyle Characteristics that influence spend in various
industries may include, for example and without limitation, credit
bureau tenure, age, gender, disposable income, geographic location,
household size, number of transaction cards in a household, size of
total spending wallet, and other third party data. FIG. 5 is a
chart illustrating how the total size of a consumer's wallet is
indicative of travel-related spend, restaurant-related spend, and
everyday spend. As illustrated, travel-related spend has the
strongest correlation with the total size of the consumer's wallet.
FIG. 6 is a chart illustrating how credit bureau tenure is
indicative of travel-related spend and restaurant-related spend. As
illustrated, travel- and restaurant-related spend are significantly
lower for consumers having low tenure with the bureau, and
relatively higher for high tenure consumers. FIG. 7 is a chart
illustrating how gender is indicative of travel-related spend and
restaurant-related spend. As illustrated, travel- and
restaurant-related spend is higher for males as compared to
females. FIG. 8 is a chart illustrating the relationship between
household size and everyday spend. As illustrated, everyday spend
varies significantly with household size. Similarly, FIG. 9 is a
chart illustrating the relationship between the number of active
transaction cards in a household and everyday spend. As
illustrated, everyday spend varies significantly with the number of
active household cards.
After the lifestyle characteristics have been identified, method
100 proceeds to step 110, in which a model to determine industry
size of wallet based on lifestyle characteristics of a consumer is
created. In a first embodiment, the model simply identifies a
typical industry size of wallet for consumers having certain
lifestyle characteristics, based on the sizes of spending wallets
of analyzed consumers sharing those lifestyle characteristics. In a
second embodiment, a size of wallet algorithm is identified based
on the correlations between consumers having common lifestyle
characteristics.
An example size of wallet algorithm for travel-related spend and
restaurant related-spend is defined in Equation 1: Total Industry
SoW=A+(B*Total Size of Plastic Spend Wallet)+(C*Location
Rank)+(D*Customer Tenure on Bureau)+(E*Customer Gender), where A,
B, C, D, and E are correlation factors or weights corresponding to
the importance of the associated lifestyle characteristics. A, B,
C, D, and E may vary depending on whether the algorithm is used to
determine, for example, travel size of wallet or restaurant size of
wallet. FIG. 10 is a graph illustrating the travel size of wallet
values predicted for various spend levels compared to the actual
travel size of wallet values for the various spend levels. FIG. 11
is a graph illustrating the restaurant size of wallet predicted for
various spend levels compared to the actual restaurant size of
wallet values for the various spend levels. As illustrated, this
model has a high level of prediction accuracy.
Similarly, an example everyday spend size of wallet algorithm is
defined in Equation 2: Total EDS SoW=V+(W*Total Size of Plastic
Spend Wallet)+(X*Number of Active Household Cards)+(Y*Location
Rank)+(Z*Household Size), where V, W, X, Y, and Z are correlation
factors or weights corresponding to the importance of the
associated lifestyle characteristics. FIG. 12 is a graph
illustrating the everyday spend size of wallet values predicted for
various spend levels compared to the actual everyday spend size of
wallet values for the various spend levels.
Similar modeling approaches can also be used to incorporate
interaction between industry spends into the industry size of
wallet model. For example, spend in particular industries or at
particular merchants may be indicative of spend in other industries
or at other merchants.
B. Consumer Targeting
Once a lifestyle characteristic indicative of spend in a particular
industry has been identified, the financial institution can target
consumers having that lifestyle characteristic with incentives to
increase spend related to the industry, even if those consumers
have low or medium share of wallet with the financial institution.
FIG. 2 is an exemplary method 200 for targeting consumers with
incentives to increase industry-related spend, according to an
embodiment of the present invention.
In step 202, one or more lifestyle characteristics indicative of
spend in a given industry are determined. These lifestyle
characteristics may be determined in accordance with a method such
as method 100 described above.
After step 202, method 100 proceeds to step 204. In step 204, a
consumer having one or more of the determined lifestyle
characteristics is identified. Since many lifestyle characteristics
of a consumer are typically publicly available (such as, for
example, from credit bureaus), the consumer does not need to have a
high industry share of wallet with the financial institution in
order to be identified by the financial institution. This method
can thus be used to target an individual having a low or medium
industry share of wallet with the financial institution. Since the
identified consumer has a lifestyle characteristic in common with
consumers who make purchases related to the given industry, the
financial institution can assume, without specific knowledge of the
identified consumer's industry-related spend, that the identified
consumer also makes purchases related to the given industry and
would be accepting of incentives to increase spend related to the
given industry.
After identifying the consumer having one or more lifestyle
characteristics indicative of spend in the given industry, method
200 proceeds to step 206. In step 206, the consumer is assigned an
industry size of wallet based on the consumer's lifestyle
characteristics. The industry size of wallet may be based on, for
example, industry sizes of wallet calculated in step 110 (using,
for example, Equation 1 or 2) of method 100 above.
The external size of wallet of the customer is calculated in step
210. The customer's external size of wallet may be calculated, for
example, by subtracting the magnitude of the customer's industry
spending associated with the financial institution from the
magnitude of the customer's industry size of wallet. The remaining
amount, which corresponds to spend in the industry that is not
associated with the financial institution, is also referred to
herein as the "external industry spend."
Method 200 then proceeds to step 212. In step 212, the identified
consumer is targeted with an offer (or promotion) that will incent
the consumer to increase spend related to the given industry. The
offer may vary based on, for example, the external size of wallet
calculated in step 210. If multiple consumers were identified in
step 204, the consumers may be prioritized based on the external
industry spend assigned in step 210, with consumers having a
greater external industry spend taking priority over consumers
having a smaller external industry spend.
Further, priority may be given to consumers having some minimal
share of total wallet with the institution. A minimal share of
total wallet will ensure a certain engagement level with the
financial institution which would lead to improved responses to the
spend offer. The customers can thus be optimized based on their
total share of wallet and the amount of external industry spend,
with the financial institution targeting only the most optimal
consumers.
In a first embodiment, the offer may be an offer for a new product,
which will encourage new spend related to the given industry. In
the example of the airline industry, a consumer who has a lifestyle
characteristic indicative of spend in the airline industry may be
targeted, for example, with an offer for a credit card that is
co-branded between the financial institution and an airline
company. In a second embodiment, the offer may be an incentive to
increase spending on an existing product held by the consumer. In
the example of the airline industry, a consumer who has a lifestyle
characteristic indicative of spend in the airline industry and who
also has a financial account associated with a rewards program
managed by the financial institution may be offered double reward
points for spend on airline travel.
If a consumer qualifies for multiple spend offers or incentives,
the financial institution may choose to target the consumer for the
industry with the highest value of spend incentive. To do this, the
consumer size and/or share of wallet is calculated for each
industry (using, for example, Equations 1 and 2), and the industry
having the largest size and/or share of the consumer's wallet
determines the targeted industry.
III. Example Implementations
The present invention (i.e., process 100, process 200 or any
part(s) or function(s) thereof) may be implemented using hardware,
software or a combination thereof and may be implemented in one or
more computer systems or other processing systems. However, the
manipulations performed by the present invention were often
referred to in terms, such as adding or comparing, which are
commonly associated with mental operations performed by a human
operator. No such capability of a human operator is necessary, or
desirable in most cases, in any of the operations described herein
which form part of the present invention. Rather, the operations
are machine operations. Useful machines for performing the
operation of the present invention include general purpose digital
computers or similar devices.
In fact, in one embodiment, the invention is directed toward one or
more computer systems capable of carrying out the functionality
described herein. An example of a computer system 1300 is shown in
FIG. 13.
The computer system 1300 includes one or more processors, such as
processor 1304. The processor 1304 is connected to a communication
infrastructure 1306 (e.g., a communications bus, cross-over bar, or
network). Various software embodiments are described in terms of
this exemplary computer system. After reading this description, it
will become apparent to a person skilled in the relevant art(s) how
to implement the invention using other computer systems and/or
architectures.
Computer system 1300 can include a display interface 1302 that
forwards graphics, text, and other data from the communication
infrastructure 1306 (or from a frame buffer not shown) for display
on the display unit 1330.
Computer system 1300 also includes a main memory 1308, preferably
random access memory (RAM), and may also include a secondary memory
1310. The secondary memory 1310 may include, for example, a hard
disk drive 1312 and/or a removable storage drive 1314, representing
a floppy disk drive, a magnetic tape drive, an optical disk drive,
etc. The removable storage drive 1314 reads from and/or writes to a
removable storage unit 1318 in a well known manner. Removable
storage unit 1318 represents a floppy disk, magnetic tape, optical
disk, etc. which is read by and written to by removable storage
drive 1314. As will be appreciated, the removable storage unit 1318
includes a computer usable storage medium having stored therein
computer software and/or data.
In alternative embodiments, secondary memory 1310 may include other
similar devices for allowing computer programs or other
instructions to be loaded into computer system 1300. Such devices
may include, for example, a removable storage unit 1318 and an
interface 1320. Examples of such may include a program cartridge
and cartridge interface (such as that found in video game devices),
a removable memory chip (such as an erasable programmable read only
memory (EPROM), or programmable read only memory (PROM)) and
associated socket, and other removable storage units 1318 and
interfaces 1320, which allow software and data to be transferred
from the removable storage unit 1318 to computer system 1300.
Computer system 1300 may also include a communications interface
1324. Communications interface 1324 allows software and data to be
transferred between computer system 1300 and external devices.
Examples of communications interface 1324 may include a modem, a
network interface (such as an Ethernet card), a communications
port, a Personal Computer Memory Card International Association
(PCMCIA) slot and card, etc. Software and data transferred via
communications interface 1324 are in the form of signals 1328 which
may be electronic, electromagnetic, optical or other signals
capable of being received by communications interface 1324. These
signals 1328 are provided to communications interface 1324 via a
communications path (e.g., channel) 1326. This channel 1326 carries
signals 1328 and may be implemented using wire or cable, fiber
optics, a telephone line, a cellular link, a radio frequency (RF)
link and other communications channels.
In this document, the terms "computer program medium" and "computer
usable medium" are used to generally refer to media such as
removable storage drive 1314 and a hard disk installed in hard disk
drive 1312. These computer program products provide software to
computer system 1300. The invention is directed to such computer
program products.
Computer programs (also referred to as computer control logic) are
stored in main memory 1308 and/or secondary memory 1310. Computer
programs may also be received via communications interface 1324.
Such computer programs, when executed, enable the computer system
1300 to perform the features of the present invention, as discussed
herein. In particular, the computer programs, when executed, enable
the processor 1304 to perform the features of the present
invention. Accordingly, such computer programs represent
controllers of the computer system 1300.
In an embodiment where the invention is implemented using software,
the software may be stored in a computer program product and loaded
into computer system 1300 using removable storage drive 1314, hard
drive 1312 or communications interface 1324. The control logic
(software), when executed by the processor 1304, causes the
processor 1304 to perform the functions of the invention as
described herein.
In another embodiment, the invention is implemented primarily in
hardware using, for example, hardware components such as
application specific integrated circuits (ASICs). Implementation of
the hardware state machine so as to perform the functions described
herein will be apparent to persons skilled in the relevant
art(s).
In yet another embodiment, the invention is implemented using a
combination of both hardware and software.
IV. Conclusion
While various embodiments of the present invention have been
described above, it should be understood that they have been
presented by way of example, and not limitation. It will be
apparent to persons skilled in the relevant art(s) that various
changes in form and detail can be made therein without departing
from the spirit and scope of the present invention. Thus, the
present invention should not be limited by any of the above
described exemplary embodiments, but should be defined only in
accordance with the following claims and their equivalents.
In addition, it should be understood that the figures and screen
shots illustrated in the attachments, which highlight the
functionality and advantages of the present invention, are
presented for example purposes only. The architecture of the
present invention is sufficiently flexible and configurable, such
that it may be utilized (and navigated) in ways other than that
shown in the accompanying figures.
* * * * *
References